no code implementations • 25 Feb 2024 • Viv Bone, Chris van der Heide, Kieran Mackle, Ingo H. J. Jahn, Peter M. Dower, Chris Manzie
Multifidelity models integrate data from multiple sources to produce a single approximator for the underlying process.
no code implementations • 13 Nov 2023 • Liam Hodgkinson, Chris van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney
Deep learning is renowned for its theory-practice gap, whereby principled theory typically fails to provide much beneficial guidance for implementation in practice.
no code implementations • 15 Jul 2023 • Liam Hodgkinson, Chris van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney
The problem of model selection is considered for the setting of interpolating estimators, where the number of model parameters exceeds the size of the dataset.
no code implementations • 14 Oct 2022 • Liam Hodgkinson, Chris van der Heide, Fred Roosta, Michael W. Mahoney
One prominent issue is the curse of dimensionality: it is commonly believed that the marginal likelihood should be reminiscent of cross-validation metrics and that both should deteriorate with larger input dimensions.
no code implementations • NeurIPS 2020 • Liam Hodgkinson, Chris van der Heide, Fred Roosta, Michael W. Mahoney
We introduce stochastic normalizing flows, an extension of continuous normalizing flows for maximum likelihood estimation and variational inference (VI) using stochastic differential equations (SDEs).
1 code implementation • 20 Feb 2020 • Russell Tsuchida, Tim Pearce, Chris van der Heide, Fred Roosta, Marcus Gallagher
Secondly, and more generally, we analyse the fixed-point dynamics of iterated kernels corresponding to a broad range of activation functions.